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<p>Possibly of interest:</p>
<p><span style="color: rgb(36, 41, 46); font-family: -apple-system,
system-ui, "Segoe UI", Helvetica, Arial, sans-serif,
"Apple Color Emoji", "Segoe UI Emoji",
"Segoe UI Symbol"; font-size: 16px; font-style:
normal; font-variant-ligatures: normal; font-variant-caps:
normal; font-weight: normal; letter-spacing: normal; orphans: 2;
text-align: start; text-indent: 0px; text-transform: none;
white-space: normal; widows: 2; word-spacing: 0px;
-webkit-text-stroke-width: 0px; background-color: rgb(255, 255,
255); text-decoration-style: initial; text-decoration-color:
initial; display: inline !important; float: none;">Race and
ethnicity Imputation from Disease history with Deep LEarning</span></p>
<p><a class="moz-txt-link-freetext" href="https://github.com/jisungk/riddle">https://github.com/jisungk/riddle</a><br>
</p>
Bill<br>
<br>
<div class="moz-cite-prefix">On 7/6/17 6:00 PM, Bill Ross wrote:<br>
</div>
<blockquote type="cite"
cite="mid:32b9ea32-b5dc-dfbe-04ca-36e8db30160e@cgl.ucsf.edu">Unless
the data concretely promotes discrimination, it seems
discriminatory to exclude it.
<br>
<br>
Bill
<br>
<br>
On 7/6/17 5:39 PM, Sebastian Raschka wrote:
<br>
<blockquote type="cite">I think there can be some middle ground.
I.e., adding a new, simple dataset to demonstrate regression
(maybe autmpg, wine quality, or sth like that) and use that for
the scikit-learn examples in the main documentation etc but
leave the boston dataset in the code base for now. Whether it's
a weak argument or not, it would be quite destructive to remove
the dataset altogether in the next version or so, not only
because old tutorials use it but many unit tests in many
different projects depend on it. I think it might be better to
phase it out by having a good alternative first, and I am sure
that the scikit-learn maintainers wouldn't have anything against
it if someone would update the examples/tutorials with the use
of different datasets
<br>
<br>
Best,
<br>
Sebastian
<br>
<br>
<blockquote type="cite">On Jul 6, 2017, at 7:36 PM, Juan
Nunez-Iglesias <a class="moz-txt-link-rfc2396E" href="mailto:jni.soma@gmail.com"><jni.soma@gmail.com></a> wrote:
<br>
<br>
For what it's worth: I'm sympathetic to the argument that you
can't fix the problem if you don't measure it, but I agree
with Tony that "many tutorials use it" is an extremely weak
argument. We removed Lena from scikit-image because it was the
right thing to do. I very much doubt that Boston house prices
is in more widespread use than Lena was in image processing.
<br>
<br>
You can argue about whether or not it's morally right or wrong
to include the dataset. I see merit to both arguments. But
"too many tutorials use it" is very similar in flavour to "the
economy of the South would collapse without slavery."
<br>
<br>
Regarding fair uses of the feature, I would hope that all
sklearn tutorials using the dataset mention such uses. The
potential for abuse and misinterpretation is enormous.
<br>
<br>
On 7 Jul 2017, 6:36 AM +1000, Jacob Schreiber
<a class="moz-txt-link-rfc2396E" href="mailto:jmschreiber91@gmail.com"><jmschreiber91@gmail.com></a>, wrote:
<br>
<blockquote type="cite">Hi Tony
<br>
<br>
As others have pointed out, I think that you may be
misunderstanding the purpose of that "feature." We are in
agreement that discrimination against protected classes is
not OK, and that even outside complying with the law one
should avoid discrimination, in model building or elsewhere.
However, I disagree that one does this by eliminating from
all datasets any feature that may allude to these protected
classes. As Andreas pointed out, there is a growing effort
to ensure that machine learning models are fair and benefit
the common good (such as FATML, DSSG, etc..), and from my
understanding the general consensus isn't necessarily that
simply eliminating the feature is sufficient. I think we are
in agreement that naively learning a model over a feature
set containing questionable features and calling it a day is
not okay, but as others have pointed out, having these
features present and handling them appropriately can help
guard against the model implicitly learning unfair!
<br>
</blockquote>
</blockquote>
</blockquote>
!
<br>
<blockquote type="cite"> biases (e
<br>
ven if they are not explicitly exposed to the feature).
<br>
<blockquote type="cite">
<blockquote type="cite">I would welcome the addition of the
Ames dataset to the ones supported by sklearn, but I'm not
convinced that the Boston dataset should be removed. As
Andreas pointed out, there is a benefit to having canonical
examples present so that beginners can easily follow along
with the many tutorials that have been written using them.
As Sean points out, the paper itself is trying to pull out
the connection between house price and clean air in the
presence of possible confounding variables. In a more
general sense, saying that a feature shouldn't be there
because a simple linear regression is unaffected by the
results is a bit odd because it is very common for datasets
to include irrelevant features, and handling them
appropriately is important. In addition, one could argue
that having this type of issue arise in a toy dataset has a
benefit because it exposes these types of issues to those
learning data science earlier on and allows them to keep
these issues in mind in the futur!
<br>
</blockquote>
</blockquote>
</blockquote>
e!
<br>
<blockquote type="cite"> when the
<br>
data is more serious.
<br>
<blockquote type="cite">
<blockquote type="cite">It is important for us all to keep
issues of fairness in mind when it comes to data science.
I'm glad that you're speaking out in favor of fairness and
trying to bring attention to it.
<br>
<br>
Jacob
<br>
<br>
On Thu, Jul 6, 2017 at 12:08 PM, Sean Violante
<a class="moz-txt-link-rfc2396E" href="mailto:sean.violante@gmail.com"><sean.violante@gmail.com></a> wrote:
<br>
G Reina
<br>
you make a bizarre argument. You argue that you should not
even check racism as a possible factor in house prices?
<br>
<br>
But then you yourself check whether its relevant
<br>
Then you say
<br>
<br>
"but I'd argue that it's more due to the location (near
water, near businesses, near restaurants, near parks and
recreation) than to the ethnic makeup"
<br>
<br>
Which was basically what the original authors wanted to
show too,
<br>
<br>
Harrison, D. and Rubinfeld, D.L. `Hedonic prices and the
demand for clean air', J. Environ. Economics &
Management, vol.5, 81-102, 1978.
<br>
<br>
but unless you measure ethnic make-up you cannot show that
it is not a confounder.
<br>
<br>
The term "white flight" refers to affluent white families
moving to the suburbs.. And clearly a question is
whether/how much was racism or avoiding air pollution.
<br>
<br>
<br>
<br>
<br>
<br>
On 6 Jul 2017 6:10 pm, "G Reina" <a class="moz-txt-link-rfc2396E" href="mailto:greina@eng.ucsd.edu"><greina@eng.ucsd.edu></a>
wrote:
<br>
I'd like to request that the "Boston Housing Prices" dataset
in sklearn (sklearn.datasets.load_boston) be replaced with
the "Ames Housing Prices" dataset
(<a class="moz-txt-link-freetext" href="https://ww2.amstat.org/publications/jse/v19n3/decock.pdf">https://ww2.amstat.org/publications/jse/v19n3/decock.pdf</a>).
I am willing to submit the code change if the developers
agree.
<br>
<br>
The Boston dataset has the feature "Bk is the proportion of
blacks in town". It is an incredibly racist "feature" to
include in any dataset. I think is beneath us as data
scientists.
<br>
<br>
I submit that the Ames dataset is a viable alternative for
learning regression. The author has shown that the dataset
is a more robust replacement for Boston. Ames is a 2011
regression dataset on housing prices and has more than 5
times the amount of training examples with over 7 times as
many features (none of which are morally questionable).
<br>
<br>
I welcome the community's thoughts on the matter.
<br>
<br>
Thanks.
<br>
-Tony
<br>
<br>
Here's an article I wrote on the Boston dataset:
<br>
<a class="moz-txt-link-freetext" href="https://www.linkedin.com/pulse/hidden-racism-data-science-g-anthony-reina?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3Bmu67f2GSzj5xHMpSD6M00A%3D%3D">https://www.linkedin.com/pulse/hidden-racism-data-science-g-anthony-reina?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3Bmu67f2GSzj5xHMpSD6M00A%3D%3D</a>
<br>
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